Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations15319
Missing cells0
Missing cells (%)0.0%
Duplicate rows1081
Duplicate rows (%)7.1%
Total size in memory13.5 MiB
Average record size in memory927.1 B

Variable types

Categorical18
Numeric12
Text1
DateTime1

Alerts

hotel has constant value "City Hotel" Constant
is_canceled has constant value "1" Constant
arrival_date_year has constant value "2016" Constant
required_car_parking_spaces has constant value "0" Constant
Dataset has 1081 (7.1%) duplicate rowsDuplicates
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
cancellation_ratio is highly overall correlated with previous_cancellationsHigh correlation
deposit_type is highly overall correlated with market_segmentHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
market_segment is highly overall correlated with deposit_type and 1 other fieldsHigh correlation
previous_cancellations is highly overall correlated with cancellation_ratioHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
stays_in_week_nights is highly overall correlated with total_stayHigh correlation
stays_in_weekend_nights is highly overall correlated with total_stayHigh correlation
total_stay is highly overall correlated with stays_in_week_nights and 1 other fieldsHigh correlation
adults is highly imbalanced (56.7%) Imbalance
children is highly imbalanced (79.9%) Imbalance
babies is highly imbalanced (97.8%) Imbalance
distribution_channel is highly imbalanced (80.5%) Imbalance
is_repeated_guest is highly imbalanced (96.0%) Imbalance
reserved_room_type is highly imbalanced (66.3%) Imbalance
assigned_room_type is highly imbalanced (66.2%) Imbalance
customer_type is highly imbalanced (72.2%) Imbalance
total_of_special_requests is highly imbalanced (63.0%) Imbalance
reservation_status is highly imbalanced (77.9%) Imbalance
previous_bookings_not_canceled is highly skewed (γ1 = 36.26197166) Skewed
stays_in_weekend_nights has 7804 (50.9%) zeros Zeros
stays_in_week_nights has 769 (5.0%) zeros Zeros
previous_cancellations has 14020 (91.5%) zeros Zeros
previous_bookings_not_canceled has 15255 (99.6%) zeros Zeros
booking_changes has 14471 (94.5%) zeros Zeros
days_in_waiting_list has 13243 (86.4%) zeros Zeros
cancellation_ratio has 14020 (91.5%) zeros Zeros

Reproduction

Analysis started2025-01-24 19:09:24.340285
Analysis finished2025-01-24 19:09:57.284683
Duration32.94 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

hotel
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
City Hotel
15319 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters153190
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity Hotel
2nd rowCity Hotel
3rd rowCity Hotel
4th rowCity Hotel
5th rowCity Hotel

Common Values

ValueCountFrequency (%)
City Hotel 15319
100.0%

Length

2025-01-24T19:09:57.400672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:09:57.490265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
city 15319
50.0%
hotel 15319
50.0%

Most occurring characters

ValueCountFrequency (%)
t 30638
20.0%
C 15319
10.0%
i 15319
10.0%
y 15319
10.0%
15319
10.0%
H 15319
10.0%
o 15319
10.0%
e 15319
10.0%
l 15319
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 30638
20.0%
C 15319
10.0%
i 15319
10.0%
y 15319
10.0%
15319
10.0%
H 15319
10.0%
o 15319
10.0%
e 15319
10.0%
l 15319
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 30638
20.0%
C 15319
10.0%
i 15319
10.0%
y 15319
10.0%
15319
10.0%
H 15319
10.0%
o 15319
10.0%
e 15319
10.0%
l 15319
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 30638
20.0%
C 15319
10.0%
i 15319
10.0%
y 15319
10.0%
15319
10.0%
H 15319
10.0%
o 15319
10.0%
e 15319
10.0%
l 15319
10.0%

is_canceled
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
1
15319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 15319
100.0%

Length

2025-01-24T19:09:57.616177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:09:57.707149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 15319
100.0%

Most occurring characters

ValueCountFrequency (%)
1 15319
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15319
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15319
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15319
100.0%

lead_time
Real number (ℝ)

Distinct383
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.12932
Minimum0
Maximum626
Zeros107
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:09:57.846621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q148
median113
Q3230
95-th percentile412
Maximum626
Range626
Interquartile range (IQR)182

Descriptive statistics

Standard deviation126.07863
Coefficient of variation (CV)0.83980021
Kurtosis0.43692289
Mean150.12932
Median Absolute Deviation (MAD)77
Skewness1.0054371
Sum2299831
Variance15895.821
MonotonicityNot monotonic
2025-01-24T19:09:58.041114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 200
 
1.3%
37 196
 
1.3%
277 187
 
1.2%
71 144
 
0.9%
56 141
 
0.9%
1 135
 
0.9%
188 132
 
0.9%
113 118
 
0.8%
158 117
 
0.8%
44 116
 
0.8%
Other values (373) 13833
90.3%
ValueCountFrequency (%)
0 107
0.7%
1 135
0.9%
2 69
0.5%
3 57
0.4%
4 77
0.5%
5 82
0.5%
6 71
0.5%
7 69
0.5%
8 76
0.5%
9 75
0.5%
ValueCountFrequency (%)
626 30
0.2%
605 30
0.2%
538 17
0.1%
531 17
0.1%
524 17
0.1%
517 17
0.1%
510 17
0.1%
503 17
0.1%
496 17
0.1%
489 17
0.1%

arrival_date_year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2016
15319 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters61276
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 15319
100.0%

Length

2025-01-24T19:09:58.230255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:09:58.316518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 15319
100.0%

Most occurring characters

ValueCountFrequency (%)
2 15319
25.0%
0 15319
25.0%
1 15319
25.0%
6 15319
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15319
25.0%
0 15319
25.0%
1 15319
25.0%
6 15319
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15319
25.0%
0 15319
25.0%
1 15319
25.0%
6 15319
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15319
25.0%
0 15319
25.0%
1 15319
25.0%
6 15319
25.0%

arrival_date_month
Categorical

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
October
1944 
June
1716 
September
1561 
April
1526 
May
1433 
Other values (7)
7139 

Length

Max length9
Median length7
Mean length6.0847314
Min length3

Characters and Unicode

Total characters93212
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApril
2nd rowMarch
3rd rowApril
4th rowJanuary
5th rowApril

Common Values

ValueCountFrequency (%)
October 1944
12.7%
June 1716
11.2%
September 1561
10.2%
April 1526
10.0%
May 1433
9.4%
November 1354
8.8%
August 1244
8.1%
March 1098
7.2%
December 1064
6.9%
July 1034
6.7%
Other values (2) 1345
8.8%

Length

2025-01-24T19:09:58.436058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
october 1944
12.7%
june 1716
11.2%
september 1561
10.2%
april 1526
10.0%
may 1433
9.4%
november 1354
8.8%
august 1244
8.1%
march 1098
7.2%
december 1064
6.9%
july 1034
6.7%
Other values (2) 1345
8.8%

Most occurring characters

ValueCountFrequency (%)
e 15156
16.3%
r 10805
 
11.6%
b 6836
 
7.3%
u 6583
 
7.1%
t 4749
 
5.1%
a 4308
 
4.6%
c 4106
 
4.4%
m 3979
 
4.3%
y 3812
 
4.1%
o 3298
 
3.5%
Other values (16) 29580
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15156
16.3%
r 10805
 
11.6%
b 6836
 
7.3%
u 6583
 
7.1%
t 4749
 
5.1%
a 4308
 
4.6%
c 4106
 
4.4%
m 3979
 
4.3%
y 3812
 
4.1%
o 3298
 
3.5%
Other values (16) 29580
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15156
16.3%
r 10805
 
11.6%
b 6836
 
7.3%
u 6583
 
7.1%
t 4749
 
5.1%
a 4308
 
4.6%
c 4106
 
4.4%
m 3979
 
4.3%
y 3812
 
4.1%
o 3298
 
3.5%
Other values (16) 29580
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15156
16.3%
r 10805
 
11.6%
b 6836
 
7.3%
u 6583
 
7.1%
t 4749
 
5.1%
a 4308
 
4.6%
c 4106
 
4.4%
m 3979
 
4.3%
y 3812
 
4.1%
o 3298
 
3.5%
Other values (16) 29580
31.7%

arrival_date_week_number
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.509759
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:09:58.616304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q342
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.52747
Coefficient of variation (CV)0.45840666
Kurtosis-1.1294326
Mean29.509759
Median Absolute Deviation (MAD)12
Skewness-0.092294368
Sum452060
Variance182.99245
MonotonicityNot monotonic
2025-01-24T19:09:58.840998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 622
 
4.1%
42 539
 
3.5%
45 537
 
3.5%
43 473
 
3.1%
40 468
 
3.1%
13 428
 
2.8%
23 398
 
2.6%
17 393
 
2.6%
18 390
 
2.5%
24 387
 
2.5%
Other values (43) 10684
69.7%
ValueCountFrequency (%)
1 40
 
0.3%
2 35
 
0.2%
3 64
 
0.4%
4 145
0.9%
5 146
1.0%
6 85
 
0.6%
7 115
 
0.8%
8 353
2.3%
9 206
1.3%
10 292
1.9%
ValueCountFrequency (%)
53 323
2.1%
52 148
 
1.0%
51 150
 
1.0%
50 249
1.6%
49 294
1.9%
48 194
 
1.3%
47 232
1.5%
46 381
2.5%
45 537
3.5%
44 368
2.4%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.841308
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:09:59.016690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8123628
Coefficient of variation (CV)0.55629009
Kurtosis-1.1757675
Mean15.841308
Median Absolute Deviation (MAD)8
Skewness-0.021321698
Sum242673
Variance77.657737
MonotonicityNot monotonic
2025-01-24T19:09:59.194053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 750
 
4.9%
28 692
 
4.5%
7 645
 
4.2%
20 636
 
4.2%
19 570
 
3.7%
13 566
 
3.7%
1 556
 
3.6%
15 552
 
3.6%
22 526
 
3.4%
27 525
 
3.4%
Other values (21) 9301
60.7%
ValueCountFrequency (%)
1 556
3.6%
2 504
3.3%
3 457
3.0%
4 509
3.3%
5 483
3.2%
6 428
2.8%
7 645
4.2%
8 487
3.2%
9 477
3.1%
10 334
2.2%
ValueCountFrequency (%)
31 221
 
1.4%
30 524
3.4%
29 465
3.0%
28 692
4.5%
27 525
3.4%
26 485
3.2%
25 425
2.8%
24 482
3.1%
23 295
1.9%
22 526
3.4%

stays_in_weekend_nights
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75207259
Minimum0
Maximum9
Zeros7804
Zeros (%)50.9%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:09:59.336509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91947534
Coefficient of variation (CV)1.2225885
Kurtosis5.3833554
Mean0.75207259
Median Absolute Deviation (MAD)0
Skewness1.5230743
Sum11521
Variance0.84543489
MonotonicityNot monotonic
2025-01-24T19:09:59.451484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 7804
50.9%
1 4020
26.2%
2 3265
21.3%
4 99
 
0.6%
3 76
 
0.5%
6 21
 
0.1%
5 16
 
0.1%
8 11
 
0.1%
7 5
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 7804
50.9%
1 4020
26.2%
2 3265
21.3%
3 76
 
0.5%
4 99
 
0.6%
5 16
 
0.1%
6 21
 
0.1%
7 5
 
< 0.1%
8 11
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 11
 
0.1%
7 5
 
< 0.1%
6 21
 
0.1%
5 16
 
0.1%
4 99
 
0.6%
3 76
 
0.5%
2 3265
21.3%
1 4020
26.2%
0 7804
50.9%

stays_in_week_nights
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3156211
Minimum0
Maximum22
Zeros769
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:09:59.585112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum22
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5800644
Coefficient of variation (CV)0.68235013
Kurtosis28.269022
Mean2.3156211
Median Absolute Deviation (MAD)1
Skewness3.5051945
Sum35473
Variance2.4966035
MonotonicityNot monotonic
2025-01-24T19:09:59.729641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 6058
39.5%
1 3243
21.2%
3 3112
20.3%
4 1171
 
7.6%
0 769
 
5.0%
5 652
 
4.3%
6 91
 
0.6%
7 53
 
0.3%
10 51
 
0.3%
8 33
 
0.2%
Other values (13) 86
 
0.6%
ValueCountFrequency (%)
0 769
 
5.0%
1 3243
21.2%
2 6058
39.5%
3 3112
20.3%
4 1171
 
7.6%
5 652
 
4.3%
6 91
 
0.6%
7 53
 
0.3%
8 33
 
0.2%
9 29
 
0.2%
ValueCountFrequency (%)
22 3
< 0.1%
21 6
< 0.1%
20 4
< 0.1%
19 2
 
< 0.1%
18 4
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 7
< 0.1%
14 7
< 0.1%
13 3
< 0.1%

adults
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
2
11634 
1
2805 
3
 
835
0
 
42
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 11634
75.9%
1 2805
 
18.3%
3 835
 
5.5%
0 42
 
0.3%
4 3
 
< 0.1%

Length

2025-01-24T19:09:59.908115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:00.025492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 11634
75.9%
1 2805
 
18.3%
3 835
 
5.5%
0 42
 
0.3%
4 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 11634
75.9%
1 2805
 
18.3%
3 835
 
5.5%
0 42
 
0.3%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 11634
75.9%
1 2805
 
18.3%
3 835
 
5.5%
0 42
 
0.3%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 11634
75.9%
1 2805
 
18.3%
3 835
 
5.5%
0 42
 
0.3%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 11634
75.9%
1 2805
 
18.3%
3 835
 
5.5%
0 42
 
0.3%
4 3
 
< 0.1%

children
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
0
14363 
1
 
521
2
 
430
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14363
93.8%
1 521
 
3.4%
2 430
 
2.8%
3 5
 
< 0.1%

Length

2025-01-24T19:10:00.169833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:00.272779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14363
93.8%
1 521
 
3.4%
2 430
 
2.8%
3 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14363
93.8%
1 521
 
3.4%
2 430
 
2.8%
3 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14363
93.8%
1 521
 
3.4%
2 430
 
2.8%
3 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14363
93.8%
1 521
 
3.4%
2 430
 
2.8%
3 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14363
93.8%
1 521
 
3.4%
2 430
 
2.8%
3 5
 
< 0.1%

babies
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
0
15287 
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15287
99.8%
1 32
 
0.2%

Length

2025-01-24T19:10:00.400738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:00.506651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15287
99.8%
1 32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15287
99.8%
1 32
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15287
99.8%
1 32
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15287
99.8%
1 32
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15287
99.8%
1 32
 
0.2%

meal
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1002.3 KiB
BB
12539 
SC
1872 
HB
 
908

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters30638
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 12539
81.9%
SC 1872
 
12.2%
HB 908
 
5.9%

Length

2025-01-24T19:10:00.624597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:00.727212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bb 12539
81.9%
sc 1872
 
12.2%
hb 908
 
5.9%

Most occurring characters

ValueCountFrequency (%)
B 25986
84.8%
S 1872
 
6.1%
C 1872
 
6.1%
H 908
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 25986
84.8%
S 1872
 
6.1%
C 1872
 
6.1%
H 908
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 25986
84.8%
S 1872
 
6.1%
C 1872
 
6.1%
H 908
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 25986
84.8%
S 1872
 
6.1%
C 1872
 
6.1%
H 908
 
3.0%
Distinct113
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1017.2 KiB
2025-01-24T19:10:00.933457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9955611
Min length2

Characters and Unicode

Total characters45889
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowPRT
4th rowPRT
5th rowFRA
ValueCountFrequency (%)
prt 8727
57.0%
fra 830
 
5.4%
esp 825
 
5.4%
gbr 718
 
4.7%
ita 634
 
4.1%
deu 522
 
3.4%
bra 335
 
2.2%
chn 248
 
1.6%
irl 191
 
1.2%
bel 190
 
1.2%
Other values (103) 2099
 
13.7%
2025-01-24T19:10:01.293437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 11339
24.7%
P 9715
21.2%
T 9567
20.8%
A 2408
 
5.2%
E 1864
 
4.1%
S 1387
 
3.0%
B 1277
 
2.8%
U 1168
 
2.5%
I 1011
 
2.2%
G 965
 
2.1%
Other values (16) 5188
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 11339
24.7%
P 9715
21.2%
T 9567
20.8%
A 2408
 
5.2%
E 1864
 
4.1%
S 1387
 
3.0%
B 1277
 
2.8%
U 1168
 
2.5%
I 1011
 
2.2%
G 965
 
2.1%
Other values (16) 5188
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 11339
24.7%
P 9715
21.2%
T 9567
20.8%
A 2408
 
5.2%
E 1864
 
4.1%
S 1387
 
3.0%
B 1277
 
2.8%
U 1168
 
2.5%
I 1011
 
2.2%
G 965
 
2.1%
Other values (16) 5188
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 11339
24.7%
P 9715
21.2%
T 9567
20.8%
A 2408
 
5.2%
E 1864
 
4.1%
S 1387
 
3.0%
B 1277
 
2.8%
U 1168
 
2.5%
I 1011
 
2.2%
G 965
 
2.1%
Other values (16) 5188
11.3%

market_segment
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Online TA
7131 
Offline TA/TO
3887 
Groups
3488 
Direct
 
496
Corporate
 
288

Length

Max length13
Median length9
Mean length9.2328481
Min length6

Characters and Unicode

Total characters141438
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline TA/TO
2nd rowOffline TA/TO
3rd rowOffline TA/TO
4th rowOffline TA/TO
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 7131
46.6%
Offline TA/TO 3887
25.4%
Groups 3488
22.8%
Direct 496
 
3.2%
Corporate 288
 
1.9%
Aviation 29
 
0.2%

Length

2025-01-24T19:10:01.440269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:01.571514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
online 7131
27.1%
ta 7131
27.1%
offline 3887
14.8%
ta/to 3887
14.8%
groups 3488
13.2%
direct 496
 
1.9%
corporate 288
 
1.1%
aviation 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 18178
12.9%
O 14905
10.5%
T 14905
10.5%
e 11802
8.3%
i 11572
8.2%
A 11047
7.8%
l 11018
7.8%
11018
7.8%
f 7774
 
5.5%
r 4560
 
3.2%
Other values (12) 24659
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 18178
12.9%
O 14905
10.5%
T 14905
10.5%
e 11802
8.3%
i 11572
8.2%
A 11047
7.8%
l 11018
7.8%
11018
7.8%
f 7774
 
5.5%
r 4560
 
3.2%
Other values (12) 24659
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 18178
12.9%
O 14905
10.5%
T 14905
10.5%
e 11802
8.3%
i 11572
8.2%
A 11047
7.8%
l 11018
7.8%
11018
7.8%
f 7774
 
5.5%
r 4560
 
3.2%
Other values (12) 24659
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 18178
12.9%
O 14905
10.5%
T 14905
10.5%
e 11802
8.3%
i 11572
8.2%
A 11047
7.8%
l 11018
7.8%
11018
7.8%
f 7774
 
5.5%
r 4560
 
3.2%
Other values (12) 24659
17.4%

distribution_channel
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
TA/TO
14412 
Direct
 
508
Corporate
 
377
GDS
 
22

Length

Max length9
Median length5
Mean length5.128729
Min length3

Characters and Unicode

Total characters78567
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA/TO
2nd rowTA/TO
3rd rowTA/TO
4th rowTA/TO
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 14412
94.1%
Direct 508
 
3.3%
Corporate 377
 
2.5%
GDS 22
 
0.1%

Length

2025-01-24T19:10:01.741739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:01.849622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 14412
94.1%
direct 508
 
3.3%
corporate 377
 
2.5%
gds 22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 28824
36.7%
A 14412
18.3%
/ 14412
18.3%
O 14412
18.3%
r 1262
 
1.6%
e 885
 
1.1%
t 885
 
1.1%
o 754
 
1.0%
D 530
 
0.7%
i 508
 
0.6%
Other values (6) 1683
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 28824
36.7%
A 14412
18.3%
/ 14412
18.3%
O 14412
18.3%
r 1262
 
1.6%
e 885
 
1.1%
t 885
 
1.1%
o 754
 
1.0%
D 530
 
0.7%
i 508
 
0.6%
Other values (6) 1683
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 28824
36.7%
A 14412
18.3%
/ 14412
18.3%
O 14412
18.3%
r 1262
 
1.6%
e 885
 
1.1%
t 885
 
1.1%
o 754
 
1.0%
D 530
 
0.7%
i 508
 
0.6%
Other values (6) 1683
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 28824
36.7%
A 14412
18.3%
/ 14412
18.3%
O 14412
18.3%
r 1262
 
1.6%
e 885
 
1.1%
t 885
 
1.1%
o 754
 
1.0%
D 530
 
0.7%
i 508
 
0.6%
Other values (6) 1683
 
2.1%

is_repeated_guest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
0
15254 
1
 
65

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15254
99.6%
1 65
 
0.4%

Length

2025-01-24T19:10:02.107868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:02.277259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15254
99.6%
1 65
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 15254
99.6%
1 65
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15254
99.6%
1 65
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15254
99.6%
1 65
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15254
99.6%
1 65
 
0.4%

previous_cancellations
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10118154
Minimum0
Maximum13
Zeros14020
Zeros (%)91.5%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:10:02.459254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51692649
Coefficient of variation (CV)5.1089012
Kurtosis362.73566
Mean0.10118154
Median Absolute Deviation (MAD)0
Skewness16.25535
Sum1550
Variance0.26721299
MonotonicityNot monotonic
2025-01-24T19:10:03.652764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14020
91.5%
1 1264
 
8.3%
13 10
 
0.1%
11 8
 
0.1%
6 7
 
< 0.1%
2 6
 
< 0.1%
3 3
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 14020
91.5%
1 1264
 
8.3%
2 6
 
< 0.1%
3 3
 
< 0.1%
5 1
 
< 0.1%
6 7
 
< 0.1%
11 8
 
0.1%
13 10
 
0.1%
ValueCountFrequency (%)
13 10
 
0.1%
11 8
 
0.1%
6 7
 
< 0.1%
5 1
 
< 0.1%
3 3
 
< 0.1%
2 6
 
< 0.1%
1 1264
 
8.3%
0 14020
91.5%

previous_bookings_not_canceled
Real number (ℝ)

Skewed  Zeros 

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.039689275
Minimum0
Maximum58
Zeros15255
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:10:03.851986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0103224
Coefficient of variation (CV)25.455804
Kurtosis1552.1768
Mean0.039689275
Median Absolute Deviation (MAD)0
Skewness36.261972
Sum608
Variance1.0207513
MonotonicityNot monotonic
2025-01-24T19:10:04.071066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 15255
99.6%
1 21
 
0.1%
25 8
 
0.1%
4 6
 
< 0.1%
5 6
 
< 0.1%
3 4
 
< 0.1%
2 3
 
< 0.1%
6 3
 
< 0.1%
12 3
 
< 0.1%
11 2
 
< 0.1%
Other values (8) 8
 
0.1%
ValueCountFrequency (%)
0 15255
99.6%
1 21
 
0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 6
 
< 0.1%
5 6
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
58 1
 
< 0.1%
48 1
 
< 0.1%
44 1
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
25 8
0.1%
15 1
 
< 0.1%
12 3
 
< 0.1%
11 2
 
< 0.1%
10 1
 
< 0.1%

reserved_room_type
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
A
12414 
D
2130 
F
 
377
B
 
204
E
 
147
Other values (2)
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowA
3rd rowA
4th rowA
5th rowD

Common Values

ValueCountFrequency (%)
A 12414
81.0%
D 2130
 
13.9%
F 377
 
2.5%
B 204
 
1.3%
E 147
 
1.0%
G 43
 
0.3%
C 4
 
< 0.1%

Length

2025-01-24T19:10:04.382373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:04.568911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 12414
81.0%
d 2130
 
13.9%
f 377
 
2.5%
b 204
 
1.3%
e 147
 
1.0%
g 43
 
0.3%
c 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 12414
81.0%
D 2130
 
13.9%
F 377
 
2.5%
B 204
 
1.3%
E 147
 
1.0%
G 43
 
0.3%
C 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 12414
81.0%
D 2130
 
13.9%
F 377
 
2.5%
B 204
 
1.3%
E 147
 
1.0%
G 43
 
0.3%
C 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 12414
81.0%
D 2130
 
13.9%
F 377
 
2.5%
B 204
 
1.3%
E 147
 
1.0%
G 43
 
0.3%
C 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 12414
81.0%
D 2130
 
13.9%
F 377
 
2.5%
B 204
 
1.3%
E 147
 
1.0%
G 43
 
0.3%
C 4
 
< 0.1%

assigned_room_type
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
A
12218 
D
2188 
F
 
385
B
 
293
E
 
172
Other values (3)
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowA
3rd rowB
4th rowA
5th rowD

Common Values

ValueCountFrequency (%)
A 12218
79.8%
D 2188
 
14.3%
F 385
 
2.5%
B 293
 
1.9%
E 172
 
1.1%
G 46
 
0.3%
C 11
 
0.1%
K 6
 
< 0.1%

Length

2025-01-24T19:10:04.869584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:05.115753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 12218
79.8%
d 2188
 
14.3%
f 385
 
2.5%
b 293
 
1.9%
e 172
 
1.1%
g 46
 
0.3%
c 11
 
0.1%
k 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 12218
79.8%
D 2188
 
14.3%
F 385
 
2.5%
B 293
 
1.9%
E 172
 
1.1%
G 46
 
0.3%
C 11
 
0.1%
K 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 12218
79.8%
D 2188
 
14.3%
F 385
 
2.5%
B 293
 
1.9%
E 172
 
1.1%
G 46
 
0.3%
C 11
 
0.1%
K 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 12218
79.8%
D 2188
 
14.3%
F 385
 
2.5%
B 293
 
1.9%
E 172
 
1.1%
G 46
 
0.3%
C 11
 
0.1%
K 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 12218
79.8%
D 2188
 
14.3%
F 385
 
2.5%
B 293
 
1.9%
E 172
 
1.1%
G 46
 
0.3%
C 11
 
0.1%
K 6
 
< 0.1%

booking_changes
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.089039755
Minimum0
Maximum14
Zeros14471
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:10:05.354068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44244022
Coefficient of variation (CV)4.9690189
Kurtosis123.29283
Mean0.089039755
Median Absolute Deviation (MAD)0
Skewness8.3273029
Sum1364
Variance0.19575335
MonotonicityNot monotonic
2025-01-24T19:10:05.667090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14471
94.5%
1 502
 
3.3%
2 256
 
1.7%
3 52
 
0.3%
4 20
 
0.1%
5 8
 
0.1%
6 6
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 14471
94.5%
1 502
 
3.3%
2 256
 
1.7%
3 52
 
0.3%
4 20
 
0.1%
5 8
 
0.1%
6 6
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 6
 
< 0.1%
5 8
 
0.1%
4 20
 
0.1%
3 52
 
0.3%
2 256
1.7%
1 502
3.3%

deposit_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No Deposit
9434 
Non Refund
5884 
Refundable
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters153190
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 9434
61.6%
Non Refund 5884
38.4%
Refundable 1
 
< 0.1%

Length

2025-01-24T19:10:05.834918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:05.937659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 9434
30.8%
deposit 9434
30.8%
non 5884
19.2%
refund 5884
19.2%
refundable 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 24752
16.2%
e 15320
10.0%
N 15318
10.0%
15318
10.0%
n 11769
7.7%
i 9434
 
6.2%
t 9434
 
6.2%
s 9434
 
6.2%
p 9434
 
6.2%
D 9434
 
6.2%
Other values (7) 23543
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 24752
16.2%
e 15320
10.0%
N 15318
10.0%
15318
10.0%
n 11769
7.7%
i 9434
 
6.2%
t 9434
 
6.2%
s 9434
 
6.2%
p 9434
 
6.2%
D 9434
 
6.2%
Other values (7) 23543
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 24752
16.2%
e 15320
10.0%
N 15318
10.0%
15318
10.0%
n 11769
7.7%
i 9434
 
6.2%
t 9434
 
6.2%
s 9434
 
6.2%
p 9434
 
6.2%
D 9434
 
6.2%
Other values (7) 23543
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 24752
16.2%
e 15320
10.0%
N 15318
10.0%
15318
10.0%
n 11769
7.7%
i 9434
 
6.2%
t 9434
 
6.2%
s 9434
 
6.2%
p 9434
 
6.2%
D 9434
 
6.2%
Other values (7) 23543
15.4%

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct74
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1874143
Minimum0
Maximum391
Zeros13243
Zeros (%)86.4%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:10:06.089706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile55
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34.640895
Coefficient of variation (CV)3.7704727
Kurtosis57.981462
Mean9.1874143
Median Absolute Deviation (MAD)0
Skewness6.6773851
Sum140742
Variance1199.9916
MonotonicityNot monotonic
2025-01-24T19:10:06.304025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13243
86.4%
39 225
 
1.5%
31 122
 
0.8%
44 108
 
0.7%
35 93
 
0.6%
46 87
 
0.6%
45 65
 
0.4%
41 63
 
0.4%
62 60
 
0.4%
3 59
 
0.4%
Other values (64) 1194
 
7.8%
ValueCountFrequency (%)
0 13243
86.4%
1 2
 
< 0.1%
3 59
 
0.4%
4 7
 
< 0.1%
5 1
 
< 0.1%
8 4
 
< 0.1%
9 12
 
0.1%
10 26
 
0.2%
11 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
391 45
0.3%
379 9
 
0.1%
330 1
 
< 0.1%
236 6
 
< 0.1%
224 6
 
< 0.1%
223 25
0.2%
215 13
 
0.1%
207 5
 
< 0.1%
193 1
 
< 0.1%
187 23
0.2%

customer_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Transient
13538 
Transient-Party
1685 
Contract
 
89
Group
 
7

Length

Max length15
Median length9
Mean length9.6523272
Min length5

Characters and Unicode

Total characters147864
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient-Party

Common Values

ValueCountFrequency (%)
Transient 13538
88.4%
Transient-Party 1685
 
11.0%
Contract 89
 
0.6%
Group 7
 
< 0.1%

Length

2025-01-24T19:10:06.491536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:06.605777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
transient 13538
88.4%
transient-party 1685
 
11.0%
contract 89
 
0.6%
group 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 30535
20.7%
t 17086
11.6%
r 17004
11.5%
a 16997
11.5%
T 15223
10.3%
s 15223
10.3%
i 15223
10.3%
e 15223
10.3%
y 1685
 
1.1%
- 1685
 
1.1%
Other values (7) 1980
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 147864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 30535
20.7%
t 17086
11.6%
r 17004
11.5%
a 16997
11.5%
T 15223
10.3%
s 15223
10.3%
i 15223
10.3%
e 15223
10.3%
y 1685
 
1.1%
- 1685
 
1.1%
Other values (7) 1980
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 147864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 30535
20.7%
t 17086
11.6%
r 17004
11.5%
a 16997
11.5%
T 15223
10.3%
s 15223
10.3%
i 15223
10.3%
e 15223
10.3%
y 1685
 
1.1%
- 1685
 
1.1%
Other values (7) 1980
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 147864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 30535
20.7%
t 17086
11.6%
r 17004
11.5%
a 16997
11.5%
T 15223
10.3%
s 15223
10.3%
i 15223
10.3%
e 15223
10.3%
y 1685
 
1.1%
- 1685
 
1.1%
Other values (7) 1980
 
1.3%

adr
Real number (ℝ)

Distinct1746
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.82577
Minimum26.35
Maximum306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:10:06.755580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.35
5-th percentile62
Q176.5
median99.45
Q3120
95-th percentile160
Maximum306
Range279.65
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation32.840409
Coefficient of variation (CV)0.31937917
Kurtosis2.0607109
Mean102.82577
Median Absolute Deviation (MAD)21.55
Skewness1.0801404
Sum1575188
Variance1078.4925
MonotonicityIncreasing
2025-01-24T19:10:06.939132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 939
 
6.1%
75 788
 
5.1%
120 563
 
3.7%
100 508
 
3.3%
65 464
 
3.0%
110 450
 
2.9%
130 408
 
2.7%
90 357
 
2.3%
80 352
 
2.3%
95 299
 
2.0%
Other values (1736) 10191
66.5%
ValueCountFrequency (%)
26.35 1
 
< 0.1%
38.67 1
 
< 0.1%
41.76 1
 
< 0.1%
43 1
 
< 0.1%
44.6 1
 
< 0.1%
45 4
< 0.1%
47.43 2
< 0.1%
47.67 1
 
< 0.1%
48.5 1
 
< 0.1%
48.6 1
 
< 0.1%
ValueCountFrequency (%)
306 1
< 0.1%
299.33 1
< 0.1%
294 1
< 0.1%
288 1
< 0.1%
285.17 2
< 0.1%
284.1 1
< 0.1%
283 1
< 0.1%
279.2 1
< 0.1%
278.9 1
< 0.1%
277.5 1
< 0.1%

required_car_parking_spaces
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
0
15319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15319
100.0%

Length

2025-01-24T19:10:07.104236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:07.195580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15319
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15319
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15319
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15319
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15319
100.0%

total_of_special_requests
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size987.4 KiB
0
12607 
1
1951 
2
 
645
3
 
106
4
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15319
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12607
82.3%
1 1951
 
12.7%
2 645
 
4.2%
3 106
 
0.7%
4 10
 
0.1%

Length

2025-01-24T19:10:07.323724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:07.434465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 12607
82.3%
1 1951
 
12.7%
2 645
 
4.2%
3 106
 
0.7%
4 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12607
82.3%
1 1951
 
12.7%
2 645
 
4.2%
3 106
 
0.7%
4 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12607
82.3%
1 1951
 
12.7%
2 645
 
4.2%
3 106
 
0.7%
4 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12607
82.3%
1 1951
 
12.7%
2 645
 
4.2%
3 106
 
0.7%
4 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12607
82.3%
1 1951
 
12.7%
2 645
 
4.2%
3 106
 
0.7%
4 10
 
0.1%

reservation_status
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Canceled
14775 
No-Show
 
544

Length

Max length8
Median length8
Mean length7.9644885
Min length7

Characters and Unicode

Total characters122008
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanceled
2nd rowNo-Show
3rd rowNo-Show
4th rowNo-Show
5th rowCanceled

Common Values

ValueCountFrequency (%)
Canceled 14775
96.4%
No-Show 544
 
3.6%

Length

2025-01-24T19:10:07.576375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-24T19:10:07.676481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
canceled 14775
96.4%
no-show 544
 
3.6%

Most occurring characters

ValueCountFrequency (%)
e 29550
24.2%
C 14775
12.1%
a 14775
12.1%
n 14775
12.1%
c 14775
12.1%
l 14775
12.1%
d 14775
12.1%
o 1088
 
0.9%
N 544
 
0.4%
- 544
 
0.4%
Other values (3) 1632
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29550
24.2%
C 14775
12.1%
a 14775
12.1%
n 14775
12.1%
c 14775
12.1%
l 14775
12.1%
d 14775
12.1%
o 1088
 
0.9%
N 544
 
0.4%
- 544
 
0.4%
Other values (3) 1632
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29550
24.2%
C 14775
12.1%
a 14775
12.1%
n 14775
12.1%
c 14775
12.1%
l 14775
12.1%
d 14775
12.1%
o 1088
 
0.9%
N 544
 
0.4%
- 544
 
0.4%
Other values (3) 1632
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29550
24.2%
C 14775
12.1%
a 14775
12.1%
n 14775
12.1%
c 14775
12.1%
l 14775
12.1%
d 14775
12.1%
o 1088
 
0.9%
N 544
 
0.4%
- 544
 
0.4%
Other values (3) 1632
 
1.3%
Distinct429
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size239.4 KiB
Minimum2015-01-12 00:00:00
Maximum2016-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-24T19:10:07.806111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:10:08.022490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_stay
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0676937
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:10:08.209770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum30
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0844052
Coefficient of variation (CV)0.67946977
Kurtosis36.161564
Mean3.0676937
Median Absolute Deviation (MAD)1
Skewness4.3402408
Sum46994
Variance4.3447449
MonotonicityNot monotonic
2025-01-24T19:10:08.389710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3 4684
30.6%
2 4577
29.9%
1 2085
13.6%
4 1934
12.6%
5 777
 
5.1%
6 527
 
3.4%
7 387
 
2.5%
8 84
 
0.5%
10 67
 
0.4%
9 44
 
0.3%
Other values (20) 153
 
1.0%
ValueCountFrequency (%)
1 2085
13.6%
2 4577
29.9%
3 4684
30.6%
4 1934
12.6%
5 777
 
5.1%
6 527
 
3.4%
7 387
 
2.5%
8 84
 
0.5%
9 44
 
0.3%
10 67
 
0.4%
ValueCountFrequency (%)
30 3
 
< 0.1%
29 8
0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 3
 
< 0.1%
25 1
 
< 0.1%
24 3
 
< 0.1%
23 1
 
< 0.1%
22 2
 
< 0.1%
21 5
< 0.1%

cancellation_ratio
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083170112
Minimum0
Maximum0.99999833
Zeros14020
Zeros (%)91.5%
Negative0
Negative (%)0.0%
Memory size239.4 KiB
2025-01-24T19:10:08.543878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.99999
Maximum0.99999833
Range0.99999833
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.27539279
Coefficient of variation (CV)3.311199
Kurtosis7.144159
Mean0.083170112
Median Absolute Deviation (MAD)0
Skewness3.0215973
Sum1274.083
Variance0.07584119
MonotonicityNot monotonic
2025-01-24T19:10:08.715093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 14020
91.5%
0.9999900001 1250
 
8.2%
0.9285707653 10
 
0.1%
0.3055554707 8
 
0.1%
0.9999983333 6
 
< 0.1%
0.4999975 4
 
< 0.1%
0.1666663889 3
 
< 0.1%
0.3333327778 2
 
< 0.1%
0.03333332222 1
 
< 0.1%
0.3333322222 1
 
< 0.1%
Other values (14) 14
 
0.1%
ValueCountFrequency (%)
0 14020
91.5%
0.03333332222 1
 
< 0.1%
0.06382977365 1
 
< 0.1%
0.06896549346 1
 
< 0.1%
0.08333326389 1
 
< 0.1%
0.09374998535 1
 
< 0.1%
0.09433960484 1
 
< 0.1%
0.1111109877 1
 
< 0.1%
0.1428569388 1
 
< 0.1%
0.1428570408 1
 
< 0.1%
ValueCountFrequency (%)
0.9999983333 6
 
< 0.1%
0.9999900001 1250
8.2%
0.9285707653 10
 
0.1%
0.6666644445 1
 
< 0.1%
0.4999975 4
 
< 0.1%
0.4285708163 1
 
< 0.1%
0.3333327778 2
 
< 0.1%
0.3333322222 1
 
< 0.1%
0.3055554707 8
 
0.1%
0.2857138776 1
 
< 0.1%

Interactions

2025-01-24T19:09:54.470588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:28.277094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:30.551880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:32.507996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:35.155836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:37.975018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:39.919380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:41.997501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:44.732871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:46.835556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:49.632575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:52.465575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:54.649770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:28.465021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:32.708330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:38.137350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:54.800080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:30.864105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:32.867016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:35.677471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:38.287386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:40.275771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:28.837323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:53.811574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:55.940356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:29.937580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:32.037131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:34.381531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:53.983966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:56.094556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:30.095460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:34.675406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:37.541350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:41.638998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-24T19:09:46.472550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:49.053159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:52.151984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:54.149256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:56.249555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:30.304592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:32.353772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:34.894345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:37.802803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:39.764936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:41.839309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:44.563936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:46.668230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:49.321160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:52.308745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-24T19:09:54.308424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-24T19:10:08.893234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adradultsarrival_date_day_of_montharrival_date_montharrival_date_week_numberassigned_room_typebabiesbooking_changescancellation_ratiochildrencustomer_typedays_in_waiting_listdeposit_typedistribution_channelis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsreservation_statusreserved_room_typestays_in_week_nightsstays_in_weekend_nightstotal_of_special_requeststotal_stay
adr1.0000.225-0.0180.1670.0950.3420.0430.065-0.1590.3640.080-0.1020.2390.0820.052-0.3720.2320.154-0.058-0.1600.0390.3750.0730.1190.0960.132
adults0.2251.0000.0450.1180.1180.3280.0200.0170.0450.1870.0860.0390.1350.0660.0820.1190.1560.1140.0250.0120.1170.3550.0530.0690.0640.070
arrival_date_day_of_month-0.0180.0451.0000.153-0.0600.0280.0000.0190.0310.0270.0800.0380.1010.0900.043-0.0470.0990.075-0.0070.0310.0380.029-0.0300.0320.030-0.004
arrival_date_month0.1670.1180.1531.0000.8260.0570.0230.0240.1550.0860.1040.1420.1470.0950.0340.2220.1530.1090.0240.0470.1550.0690.0700.0780.1140.082
arrival_date_week_number0.0950.118-0.0600.8261.0000.0500.019-0.005-0.2730.0800.115-0.2300.1500.0820.0320.2110.1440.111-0.007-0.2720.1500.0620.0730.0430.1170.074
assigned_room_type0.3420.3280.0280.0570.0501.0000.0790.0380.0520.4850.0950.0530.2680.1710.0110.1060.2300.1280.0000.0000.1120.8810.0690.0640.1010.085
babies0.0430.0200.0000.0230.0190.0791.0000.0490.0000.0200.0000.0000.0340.0540.0000.0150.0580.0000.0000.0000.0070.0640.0000.0120.0940.019
booking_changes0.0650.0170.0190.024-0.0050.0380.0491.000-0.0640.0440.019-0.0680.0790.0250.000-0.0730.0550.0290.019-0.0630.0570.0420.0220.0620.0560.053
cancellation_ratio-0.1590.0450.0310.155-0.2730.0520.000-0.0641.0000.0380.1050.3720.2000.1140.4730.0920.1710.0940.1241.0000.0560.052-0.049-0.1030.046-0.096
children0.3640.1870.0270.0860.0800.4850.0200.0440.0381.0000.0230.0370.1430.0460.0000.0810.1450.0610.0000.0000.0190.4950.0480.0470.0790.061
customer_type0.0800.0860.0800.1040.1150.0950.0000.0190.1050.0231.0000.1350.1970.1550.2350.0760.1870.0770.0370.0550.0820.0920.0310.0320.0880.049
days_in_waiting_list-0.1020.0390.0380.142-0.2300.0530.000-0.0680.3720.0370.1351.0000.2020.0360.0000.2490.1720.1460.0260.3720.0510.0530.019-0.1010.064-0.027
deposit_type0.2390.1350.1010.1470.1500.2680.0340.0790.2000.1430.1970.2021.0000.1220.0500.4330.5710.2100.0020.0200.1510.2600.1520.1650.2550.208
distribution_channel0.0820.0660.0900.0950.0820.1710.0540.0250.1140.0460.1550.0360.1221.0000.2300.1190.7160.0590.0950.0660.1440.1830.0880.0780.0350.084
is_repeated_guest0.0520.0820.0430.0340.0320.0110.0000.0000.4730.0000.2350.0000.0500.2301.0000.0620.2540.0260.4640.4810.0660.0000.0410.0300.0050.034
lead_time-0.3720.119-0.0470.2220.2110.1060.015-0.0730.0920.0810.0760.2490.4330.1190.0621.0000.2940.181-0.0480.0920.1620.1090.062-0.1360.123-0.028
market_segment0.2320.1560.0990.1530.1440.2300.0580.0550.1710.1450.1870.1720.5710.7160.2540.2941.0000.2730.0860.0660.1760.2410.1520.1420.2110.162
meal0.1540.1140.0750.1090.1110.1280.0000.0290.0940.0610.0770.1460.2100.0590.0260.1810.2731.0000.0000.0000.0540.1280.0660.0970.1000.055
previous_bookings_not_canceled-0.0580.025-0.0070.024-0.0070.0000.0000.0190.1240.0000.0370.0260.0020.0950.464-0.0480.0860.0001.0000.1450.0620.000-0.073-0.0090.015-0.070
previous_cancellations-0.1600.0120.0310.047-0.2720.0000.000-0.0631.0000.0000.0550.3720.0200.0660.4810.0920.0660.0000.1451.0000.0140.014-0.051-0.1020.000-0.097
reservation_status0.0390.1170.0380.1550.1500.1120.0070.0570.0560.0190.0820.0510.1510.1440.0660.1620.1760.0540.0620.0141.0000.0460.1650.1540.1020.158
reserved_room_type0.3750.3550.0290.0690.0620.8810.0640.0420.0520.4950.0920.0530.2600.1830.0000.1090.2410.1280.0000.0140.0461.0000.0800.0690.0990.096
stays_in_week_nights0.0730.053-0.0300.0700.0730.0690.0000.022-0.0490.0480.0310.0190.1520.0880.0410.0620.1520.066-0.073-0.0510.1650.0801.0000.0170.0510.787
stays_in_weekend_nights0.1190.0690.0320.0780.0430.0640.0120.062-0.1030.0470.032-0.1010.1650.0780.030-0.1360.1420.097-0.009-0.1020.1540.0690.0171.0000.0590.575
total_of_special_requests0.0960.0640.0300.1140.1170.1010.0940.0560.0460.0790.0880.0640.2550.0350.0050.1230.2110.1000.0150.0000.1020.0990.0510.0591.0000.074
total_stay0.1320.070-0.0040.0820.0740.0850.0190.053-0.0960.0610.049-0.0270.2080.0840.034-0.0280.1620.055-0.070-0.0970.1580.0960.7870.5750.0741.000

Missing values

2025-01-24T19:09:56.598264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-24T19:09:57.005543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typedays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datetotal_staycancellation_ratio
4City Hotel1312016April172324210BBPRTOffline TA/TOTA/TO000DD0No Deposit0Transient26.3500Canceled23/03/201660.0
30City Hotel1122016March142812200BBPRTOffline TA/TOTA/TO000AA0No Deposit0Transient38.6700No-Show28/03/201630.0
85City Hotel1602016April15923300BBPRTOffline TA/TOTA/TO000AB0No Deposit0Transient41.7601No-Show09/04/201650.0
92City Hotel1162016January63122100BBPRTOffline TA/TOTA/TO000AA0No Deposit0Transient43.0000No-Show31/01/201640.0
108City Hotel102016April15321200BBFRAOnline TATA/TO000DD0No Deposit0Transient-Party44.6000Canceled03/04/201630.0
112City Hotel102016April182801100BBPRTOffline TA/TOTA/TO000AD0No Deposit0Transient-Party45.0002Canceled28/04/201610.0
113City Hotel102016July312701100BBPRTOffline TA/TOTA/TO000AA0No Deposit0Transient45.0001Canceled27/07/201610.0
114City Hotel102016July312701100BBPRTOffline TA/TOTA/TO000AA0No Deposit0Transient45.0001Canceled27/07/201610.0
115City Hotel102016October442801100BBPRTOffline TA/TOTA/TO000AA0No Deposit0Transient45.0001No-Show28/10/201610.0
153City Hotel1542016March10104200SCPRTOnline TATA/TO000AA0No Deposit0Transient47.4300Canceled07/01/201640.0
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typedays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datetotal_staycancellation_ratio
37462City Hotel1362016July312714220BBPRTOnline TATA/TO000GG0No Deposit0Transient278.9000Canceled22/06/201650.0
37463City Hotel1292016September402814100BBPRTOnline TATA/TO000GG2No Deposit0Transient279.2001Canceled28/09/201650.0
37465City Hotel102016August341601220BBPRTDirectDirect000FF0No Deposit0Transient283.0003No-Show16/08/201610.0
37467City Hotel1222016September381701120BBEGYOnline TATA/TO000BB0No Deposit0Transient284.1000Canceled26/08/201610.0
37469City Hotel1242016May212121310BBPRTOnline TATA/TO000GG0No Deposit0Transient285.1700Canceled03/05/201630.0
37470City Hotel1242016May212121310BBPRTOnline TATA/TO000GG0No Deposit0Transient285.1700Canceled03/05/201630.0
37472City Hotel1222016August341902220HBESPOnline TATA/TO000FF0No Deposit0Transient288.0002Canceled02/08/201620.0
37474City Hotel1262016October40122220HBBELOnline TATA/TO000FF0No Deposit0Transient294.0002Canceled16/09/201640.0
37479City Hotel1212016July301721400BBPRTOnline TATA/TO000GG0No Deposit0Transient299.3300Canceled07/07/201630.0
37485City Hotel1112016September381612220HBFRAOnline TATA/TO000FF0No Deposit0Transient306.0000Canceled08/09/201630.0

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typedays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datetotal_staycancellation_ratio# duplicates
914City Hotel12772016November46712200BBPRTGroupsTA/TO000AA0Non Refund0Transient100.000Canceled04/04/201630.00000180
348City Hotel1682016February81702200BBPRTGroupsTA/TO010AA0Non Refund0Transient75.000Canceled06/01/201620.99999150
757City Hotel11882016June251502100BBPRTOffline TA/TOTA/TO000AA0Non Refund39Transient130.000Canceled18/01/201620.00000109
665City Hotel11582016May222402100BBPRTGroupsTA/TO000AA0Non Refund31Transient130.000Canceled18/01/201620.00000101
360City Hotel1712016June251403100BBPRTOffline TA/TOTA/TO000AA0Non Refund0Transient120.000Canceled27/04/201630.0000089
693City Hotel11662016November45103100BBPRTOffline TA/TOTA/TO000AA0Non Refund0Transient110.000Canceled13/07/201630.0000085
955City Hotel13042016November45303200BBPRTOffline TA/TOTA/TO000AA0Non Refund0Transient89.000Canceled01/02/201630.0000085
956City Hotel13052016November45412200BBPRTOffline TA/TOTA/TO000AA0Non Refund0Transient89.000Canceled01/02/201630.0000085
217City Hotel1372016October421303200BBPRTOffline TA/TOTA/TO000AA0No Deposit0Transient-Party105.000Canceled06/09/201630.0000084
474City Hotel1992016February81901200BBPRTCorporateCorporate010AA0No Deposit0Transient-Party80.000Canceled22/12/201510.9999969